Summary1. Gridded climatologies have become an indispensable component of bioclimatic modelling, with a range of applications spanning conservation and pest management. Such globally conformal data sets of historical and future scenario climate surfaces are required to model species potential ranges under current and future climate scenarios. 2. We developed a set of interpolated climate surfaces at 10¢ and 30¢ resolution for global land areas excluding Antarctica. Input data for the baseline climatology were gathered from the WorldClim and CRU CL1AE0 and CL2AE0 data sets. A set of future climate scenarios were generated at 10¢ resolution. For each of the historical and future scenario data sets, the full set of 35 Bioclim variables was generated. Climate variables (including relative humidity at 0900 and 1500 hours) were also generated in CLIMEX format. The Ko¨ppen-Geiger climate classification scheme was applied to the 10¢ hybrid climatology as a tool for visualizing climatic patterns and as an aid for specifying absence or background data for correlative modelling applications. 3. We tested the data set using a correlative model (MaxEnt) addressing conservation biology concerns for a rare Australian shrub, and a mechanistic niche model (CLIMEX) to map climate suitability for two invasive species. In all cases, the underlying climatology appeared to behave in a robust manner. 4. This global climate data set has the advantage over the WorldClim data set of including humidity data and an additional 16 Bioclim variables. Compared with the CRU CL2AE0 data set, the hybrid 10¢ data set includes improved precipitation estimates as well as projected climate for two global climate models running relevant greenhouse gas emission scenarios. 5. For many bioclimatic modelling purposes, there is an operational attraction to having a globally conformal historical climatology and future climate scenarios for the assessments of potential climate change impacts. Our data set is known as 'CliMond' and is available for free download from http://www.climond.org.

Seed persistence is the survival of seeds in the environment once they have reached maturity. Seed persistence allows a species, population or genotype to survive long after the death of parent plants, thus distributing genetic diversity through time. The ability to predict seed persistence accurately is critical to inform long-term weed management and flora rehabilitation programs, as well as to allow a greater understanding of plant community dynamics. Indeed, each of the 420000 seed-bearing plant species has a unique set of seed characteristics that determine its propensity to develop a persistent soil seed bank. The duration of seed persistence varies among species and populations, and depends on the physical and physiological characteristics of seeds and how they are affected by the biotic and abiotic environment. An integrated understanding of the ecophysiological mechanisms of seed persistence is essential if we are to improve our ability to predict how long seeds can survive in soils, both now and under future climatic conditions. In this review we present an holistic overview of the seed, species, climate, soil, and other site factors that contribute mechanistically to seed persistence, incorporating physiological, biochemical and ecological perspectives. We focus on current knowledge of the seed and species traits that influence seed longevity under ex situ controlled storage conditions, and explore how this inherent longevity is moderated by changeable biotic and abiotic conditions in situ, both before and after seeds are dispersed. We argue that the persistence of a given seed population in any environment depends on its resistance to exiting the seed bank via germination or death, and on its exposure to environmental conditions that are conducive to those fates. By synthesising knowledge of how the environment affects seeds to determine when and how they leave the soil seed bank into a resistance-exposure model, we provide a new framework for developing experimental and modelling approaches to predict how long seeds will persist in a range of environments.

Aim Investigate the relative abilities of different bioclimatic models and data sets to project species ranges in novel environments utilizing the natural experiment in biogeography provided by Australian Acacia species.Location Australia, South Africa. MethodsWe built bioclimatic models for Acacia cyclops and Acacia pycnantha using two discriminatory correlative models (MaxEnt and Boosted Regression Trees) and a mechanistic niche model (CLIMEX). We fitted models using two training data sets: native-range data only ('restricted') and all available global data excluding South Africa ('full'). We compared the ability of these techniques to project suitable climate for independent records of the species in South Africa. In addition, we assessed the global potential distributions of the species to projected climate change.Results All model projections assessed against their training data, the South African data and globally were statistically significant. In South Africa and globally, the additional information contained in the full data set generally improved model sensitivity, but at the expense of increased modelled prevalence, particularly in extrapolation areas for the correlative models. All models projected some climatically suitable areas in South Africa not currently occupied by the species. At the global scale, widespread and biologically unrealistic projections by the correlative models were explained by open-ended response curves, a problem which was not always addressed by broader background climate space or by the extra information in the full data set. In contrast, the global projections for CLIMEX were more conservative. Projections into 2070 indicated a polewards shift in climate suitability and a decrease in model interpolation area.Main conclusions Our results highlight the importance of carefully interpreting model projections in novel climates, particularly for correlative models. Much work is required to ensure bioclimatic models performed in a robust and ecologically plausible manner in novel climates. We explore reasons for variations between models and suggest methods and techniques for future improvements.

Accepted ArticleThis article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/gcb.13492 This article is protected by copyright. All rights reserved. Accepted ArticleThis article is protected by copyright. All rights reserved. AbstractGlobally, Phytophthora cinnamomi is listed as one of the 100 worst invasive alien species and active management is required to reduce impact and prevent spread in both horticulture and natural ecosystems. Conversely, there are regions thought to be suitable for the pathogen where no disease is observed. We developed a CLIMEX model for the global distribution of P. cinnamomi based on the pathogen's response to temperature and moisture and by incorporating extensive empirical evidence on the presence and absence of the pathogen. The CLIMEX model captured areas of climatic suitability where P. cinnamomi occurs that is congruent with all available records. The model was validated by the collection of soil samples from asymptomatic vegetation in areas projected to be suitable by the model for which there were few records. DNA was extracted and the presence or absence of P. cinnamomi determined by high throughput sequencing (HTS). While not detected using traditional isolation methods, HTS detected P. cinnamomi at higher elevations in eastern Australia and central Tasmania as projected by the CLIMEX model. Further support for the CLIMEX model was obtained by using the large dataset from southwest Australia where the proportion of positive records in an area is related to the Ecoclimatic Index value for the same area. We provide for the first time a comprehensive global map of the current P. cinnamomi distribution, an improved CLIMEX model of the distribution, and a projection to 2080 of the distribution with predicted climate change. This information provides the basis for more detailed regional scale modelling and supports risk assessment for governments to plan management of this important soil-borne plant pathogen.

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Indirect interactions are almost inevitable in any multi-species community. Understanding the implications of such interactions is a challenging task, in light of the very large number of ways species can be tied together in complex food webs. One approach to this complexity is to focus on strong interactions among a relatively small number (e.g. 3-6) of species interacting in defined configurations: community modules. In recent years, the discipline of community ecology has developed a substantial body of theory focused on such modules. Modules often clearly describe the basic features of empirical systems, particularly in simplified anthropogenic landscapes, and also help to isolate and characterize key processes driving the dynamics of more complex communities. In this chapter, we draw out a number of insights from ecological studies of modules which we believe are relevant to biological control. We emphasize in particular the module of 'shared predation', where a natural enemy attacks two or more species of prey. Theoretical studies suggest a number of 'rules of thumb', including: (i) the greatest risk to non-targets may occur from control agents that are only moderately effective on the target; (ii) targets with a high reproductive capacity can indirectly endanger non-targets; (iii) there can be transient phases of extinction risk for non-targets during the establishment phase of control agents, particularly for species with high attack rates; (iv) at a landscape scale, mobile agents can endanger the fate of non-targets at sites other than the area of control; (v) using specialist natural enemies can pose risks to non-targets, if there are generalist resident predators/parasitoids which can exploit these introduced agents. The theoretical models help to highlight circumstances when these effects should be particularly strong.

Causes of current severe declines of the deciduous oaks Quercus robur and Q. petraea in northern and central Europe and of the evergreen Q. ilex, Q. suber and other Quercus spp. in the Mediterranean area are reviewed. Factors implicated include drought, pollution, winter cold, flooding, and stress‐related attacks by insects and fungi. Additional factors in Mediterranean oak declines include changing land‐use patterns and root disease caused by the aggressive, exotic oomycete root pathogen Phytophthora cinnamomi. Under conditions of global warming the survival and degree of root disease caused by this fungus seems likely to be enhanced, while the host range of the organism might also be increased. Application of the CLIMEX climate‐matching program suggests that with a mean increase in temperatures of 1.5–3°C the fungus might considerably increase its disease activity in its existing locations, and to some extent spread northwards and eastwards. However, it seems unlikely to become significantly active in areas of Europe with colder winters such as parts of Scandinavia, Russia and the central Danube. The predictive value of research on major environmental problems such as oak declines could be enhanced by more highly coordinated European forestry research programmes.

The importance of ecological management for reducing the vulnerability of biodiversity to climate change is increasingly recognized, yet frameworks to facilitate a structured approach to climate adaptation management are lacking. We developed a conceptual framework that can guide identification of climate change impacts and adaptive management options in a given region or biome. The framework focuses on potential points of early climate change impact, and organizes these along two main axes. First, it recognizes that climate change can act at a range of ecological scales. Secondly, it emphasizes that outcomes are dependent on two potentially interacting and countervailing forces: (1) changes to environmental parameters and ecological processes brought about by climate change, and (2) responses of component systems as determined by attributes of resistance and resilience. Through this structure, the framework draws together a broad range of ecological concepts, with a novel emphasis on attributes of resistance and resilience that can temper the response of species, ecosystems and landscapes to climate change. We applied the framework to the world’s largest remaining Mediterranean-climate woodland, the ‘Great Western Woodlands’ of south-western Australia. In this relatively intact region, maintaining inherent resistance and resilience by preventing anthropogenic degradation is of highest priority and lowest risk. Limited, higher risk options such as fire management, protection of refugia and translocation of adaptive genes may be justifiable under more extreme change, hence our capacity to predict the extent of change strongly impinges on such management decisions. These conclusions may contrast with similar analyses in degraded landscapes, where natural integrity is already compromised, and existing investment in restoration may facilitate experimentation with higher risk options